Subgroup identification for treatment selection in biomarker adaptive design Data analysis, statistics and modelling
Journal
BMC Medical Research Methodology
Journal Volume
15
Journal Issue
1
Date Issued
2015
Author(s)
Chen J.J.
Abstract
Background: Advances in molecular technology have shifted new drug development toward targeted therapy for treatments expected to benefit subpopulations of patients. Adaptive signature design (ASD) has been proposed to identify the most suitable target patient subgroup to enhance efficacy of treatment effect. There are two essential aspects in the development of biomarker adaptive designs: 1) an accurate classifier to identify the most appropriate treatment for patients, and 2) statistical tests to detect treatment effect in the relevant population and subpopulations. We propose utilization of classification methods to identity patient subgroups and present a statistical testing strategy to detect treatment effects. Methods: The diagonal linear discriminant analysis (DLDA) is used to identify targeted and non-targeted subgroups. For binary endpoints, DLDA is directly applied to classify patient into two subgroups; for continuous endpoints, a two-step procedure involving model fitting and determination of a cutoff-point is used for subgroup classification. The proposed strategy includes tests for treatment effect in all patients and in a marker-positive subgroup, with a possible follow-up estimation of treatment effect in the marker-negative subgroup. The proposed method is compared to the ASD classification method using simulated datasets and two publically available cancer datasets. Results: The DLDA-based classifier performs well in terms of sensitivity, specificity, positive and negative predictive values, and accuracy in the simulation data and the two cancer datasets, with superior accuracy compared to the ASD method. The subgroup testing strategy is shown to be useful in detecting treatment effect in terms of power and control of study-wise error. Conclusion: Accuracy of a classifier is essential for adaptive designs. A poor classifier not only assigns patients to inappropriate treatments, but also reduces the power of the test, resulting in incorrect conclusions. The proposed procedure provides an effective approach for subgroup identification and subgroup analysis. ? 2015 Lu and Chen.
Other Subjects
tumor marker; adenocarcinoma; algorithm; bioassay; computer simulation; disease free survival; human; Lung Neoplasms; methodology; patient selection; personalized medicine; procedures; statistical model; Adenocarcinoma; Algorithms; Biomarkers, Tumor; Computer Simulation; Disease-Free Survival; Endpoint Determination; Humans; Lung Neoplasms; Models, Statistical; Patient Selection; Precision Medicine; Research Design
Publisher
BioMed Central Ltd.
Type
journal article